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Danish BERT fine-tuned for Detecting 'Analytical'

This model detects if a Danish text is 'subjective' or 'objective'.

It is trained and tested on Tweets and texts transcribed from the European Parliament annotated by Alexandra Institute. The model is trained with the senda package.

Here is an example of how to load the model in PyTorch using the 🤗Transformers library:

from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
tokenizer = AutoTokenizer.from_pretrained("pin/analytical")
model = AutoModelForSequenceClassification.from_pretrained("pin/analytical")

# create 'senda' sentiment analysis pipeline 
analytical_pipeline = pipeline('sentiment-analysis', model=model, tokenizer=tokenizer)

text = "Jeg synes, det er en elendig film"
# in English: 'I think, it is a terrible movie'


The senda model achieves an accuracy of 0.89 and a macro-averaged F1-score of 0.78 on a small test data set, that Alexandra Institute provides. The model can most certainly be improved, and we encourage all NLP-enthusiasts to give it their best shot - you can use the senda package to do this.


Feel free to contact author Lars Kjeldgaard on lars.kjeldgaard@eb.dk.

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Text Classification
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